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In this work, a conceptual bio-inspired parallel and distributed learningframework for the emergence of general intelligence is proposed, where agentsevolve through environmental rewards and learn throughout their lifetimewithout supervision, i.e., self-learning through embodiment. The chosen controlmechanism for agents is a biologically plausible neuron model based on spikingneural networks. Network topologies become more complex through evolution,i.e., the topology is not fixed, while the synaptic weights of the networkscannot be inherited, i.e., newborn brains are not trained and have no innateknowledge of the environment. What is subject to the evolutionary process isthe network topology, the type of neurons, and the type of learning. Thisprocess ensures that controllers that are passed through the generations havethe intrinsic ability to learn and adapt during their lifetime in mutableenvironments. We envision that the described approach may lead to the emergenceof the simplest form of artificial general intelligence.

A Conceptual Bio-Inspired Framework for the Evolution of Artificial General Intelligence